Feature Engineering in Machine Learning

All machine learning algorithms use data as the input to calibrate and generate output. Data is initially in its crudest form, requiring enhancement before feeding it to the algorithm. This input data comprises features, which are measurable properties of a process, often represented in the form of structured columns.

What is feature engineering?

Feature engineering is the process of transforming raw data into meaningful features for machine learning. It can improve the performance, accuracy, and interpretability of your machine learning models, as well as reduce the complexity and computational cost.
Features are the attributes that describe the data, such as numerical, categorical, or text values. Feature engineering involves using domain knowledge, statistical analysis, and creativity to extract, transform, combine, or generate new features that can capture the patterns, relationships, or trends in the data.

Feature engineering consists of following process

• Feature Creation:
  • It involves creating new variables which will be most helpful for our model. This can be adding or removing some features.
• Feature Transformations:
  • It is a function that transforms features from one representation to another. The goal here is to visualize data, if something is not adding up with the new features we can reduce the number of features used, speed up training, or increase the accuracy of a certain model.
• Feature Extraction:
  • Feature extraction involves combining the existing features into new ones thereby reducing the number of features in the dataset. This reduces the amount of data into manageable sizes for algorithms to process, without distorting the original relationships or relevant information.
• Exploratory Data Analysis:
  • It is a powerful tools that can be used to improve your understanding of your data, by exploring its properties. This technique is often applied when the goal is to create new hypotheses or find patterns in the data. It’s often used on large amounts of qualitative or quantitative data that haven’t been analyzed before.
• Feature Selection:
  • It involves choosing a set of features from a large collection. Selecting the important features and reducing the size of the feature set makes computation in machine learning more feasible. Feature selection also improves the quality of the output obtained from algorithms.

Feature Engineering Techniques

• Imputation:
  • The most common problems in machine learning is the absence of values in the datasets. The causes of missing values can be due to numerous issues like human error, privacy concern and interruptions in the flow of data among many. Irrespective of the cause, absence of values affects the performance of machine learning algorithms. Although there are many imputation methods, replacing missing values with the mean of the column is a common imputation method.
• One-Hot Encoding:
  • One-hot encoding is a method of assigning binary values to values in the columns. In this method, all values above the threshold are converted to 1, while all values equal to or below the threshold are converted as 0. This changes the feature values to a numerical format which is much easier for algorithms to understand without compromising the value of the information and the relationship between the variables and the feature.
• Grouping Operations:
  • Many datasets rarely fit into the simplistic arrangement of rows and columns as each column has multiple rows of an instance. To handle such cases, data is grouped in such a fashion that every variable is represented by only one row. The intention of grouping operations is to arrive at an aggregation that establishes the most viable relationship with features.
• Log Transformation:
  • A measure of asymmetry in a dataset is known as Skewness, which is defined as the extent to which a given distribution of data varies from a normal distribution. Skewness of data affects the prediction models in ML algorithms. To resolve this, Log Transformations are used to reduce the skewness of data. The less skewed distributions are, the better is the ability of algorithms to interpret patterns.
• Bag of Words:
  • It is a counting algorithm that evaluates the number of repetitions of a word in a document. This algorithm is useful in identifying similarities and differences in documents for applications like search and document classification.
• Feature Hashing:
  • Feature hashing is a technique used to scale up machine learning algorithms by vectorizing features. Feature hashing is commonly used in document classification and sentiment analysis where tokens are converted into integers. Hash values are derived by applying hash functions to features that are used as indices to map data.

Importance of feature engineering

Feature engineering can make a big difference in the quality and performance of your machine learning models. By creating features that are relevant, informative, and representative of the data, you can enhance the ability of your models to learn from the data and generalize to new situations. Feature engineering can also help you deal with common data challenges, such as missing values, outliers, imbalance, or high dimensionality.

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